Yazar "Ugurlu, Mehmet" seçeneğine göre listele

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    Attitudes and behaviors of physicians in dealing with difficult patients and relatives: a cross-sectional study in two training and research hospitals
    (Tubitak Scientific & Technological Research Council Turkey, 2017) Sandikci, Kamuran Bahar; Ustu, Yusuf; Sandikci, Mert Muhittin; Kayhan Tetik, Burcu; Isik, Derya; Ugurlu, Mehmet
    Background/aim: The aim of this study was to examine the reasons constituting the definition of 'difficult patient' and to evaluate attitudes and behaviors of physicians in coping with these patients and their relatives. Materials and methods: This cross-sectional study was conducted in May and June 2013 with 400 randomly selected physicians from different specialties working in two training and research hospitals in Ankara. A questionnaire was created by reviewing the relevant literature, by family medicine clinic, and delivered to the physicians following a pilot study. Results: In our study 92.8% of the physicians participating had experienced a negative contact with patients and/or their relatives, previously; 46.8% of the participants stated that they used their own experiences in coping with those situations. The frequency of negative communications was higher in surgical departments, increasing with average daily working hours and number of patients and decreasing with the experience of the physicians. The ways of coping with a difficult patient were nonjudgmental listening, patience, tolerance, and empathy, in declining order of importance. Conclusion: Physicians frequently experience negative communications with patients and/or relatives. Awareness of physicians about the concept of difficult patients and the causes and solutions should be enhanced.
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    Detecting white spot lesions on post-orthodontic oral photographs using deep learning based on the YOLOv5x algorithm: a pilot study
    (Bmc, 2024) Ozsunkar, Pelin Senem; Ozen, Duygu CelIk; Abdelkarim, Ahmed Z.; Duman, Sacide; Ugurlu, Mehmet; Demir, Mehmet Ridvan; Kuleli, Batuhan
    Background Deep learning model trained on a large image dataset, can be used to detect and discriminate targets with similar but not identical appearances. The aim of this study is to evaluate the post-training performance of the CNN-based YOLOv5x algorithm in the detection of white spot lesions in post-orthodontic oral photographs using the limited data available and to make a preliminary study for fully automated models that can be clinically integrated in the future.Methods A total of 435 images in JPG format were uploaded into the CranioCatch labeling software and labeled white spot lesions. The labeled images were resized to 640 x 320 while maintaining their aspect ratio before model training. The labeled images were randomly divided into three groups (Training:349 images (1589 labels), Validation:43 images (181 labels), Test:43 images (215 labels)). YOLOv5x algorithm was used to perform deep learning. The segmentation performance of the tested model was visualized and analyzed using ROC analysis and a confusion matrix. True Positive (TP), False Positive (FP), and False Negative (FN) values were determined.Results Among the test group images, there were 133 TPs, 36 FPs, and 82 FNs. The model's performance metrics include precision, recall, and F1 score values of detecting white spot lesions were 0.786, 0.618, and 0.692. The AUC value obtained from the ROC analysis was 0.712. The mAP value obtained from the Precision-Recall curve graph was 0.425.Conclusions The model's accuracy and sensitivity in detecting white spot lesions remained lower than expected for practical application, but is a promising and acceptable detection rate compared to previous study. The current study provides a preliminary insight to further improved by increasing the dataset for training, and applying modifications to the deep learning algorithm.Clinical revelance Deep learning systems can help clinicians to distinguish white spot lesions that may be missed during visual inspection.